Summary of Node Classification with Integrated Reject Option, by Uday Bhaskar et al.
Node Classification With Integrated Reject Option
by Uday Bhaskar, Jayadratha Gayen, Charu Sharma, Naresh Manwani
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to node classification in Graph Neural Networks (GNNs) is proposed, integrating a reject option for uncertainty management. The method, NCwR, allows GNNs to abstain from predictions when unsure, enabling adaptivity to reject option setting. Both cost-based and coverage-based methods are explored for classification with abstention in node classification settings using GNNs. Evaluations on three citation network datasets (Cora, Citeseer, Pubmed) demonstrate the effectiveness of NCwR compared to relevant baselines. Additionally, the method is applied to a legal judgment prediction problem on the ILDC dataset, treating nodes as legal cases and edges as citations. The model’s decision-making process is visualized to analyze which input features influence abstention decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to classify things in graphs using special kinds of neural networks (GNNs) is introduced. This method, called NCwR, lets GNNs say “I’m not sure” instead of making a wrong prediction when it’s unsure. It does this by adding a special option that allows the model to reject making a prediction when the uncertainty is high. The paper shows how well this approach works on three different kinds of datasets and compares it to other methods. It also applies this method to a problem where nodes represent legal cases and edges represent citations, showing which parts of the data influence the model’s decisions. |
Keywords
» Artificial intelligence » Classification
